{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Multiclass Support Vector Machine exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. \n",
    "\n",
    "填写并提交这份完成的工作表（包括其输出和工作表外部的任何支持代码）。\n",
    "\n",
    "For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "In this exercise you will:\n",
    "    \n",
    "- implement a fully-vectorized **loss function** for the SVM\n",
    "- implement the fully-vectorized expression for its **analytic gradient**\n",
    "- **check your implementation** using numerical gradient\n",
    "- use a validation set to **tune the learning rate and regularization** strength\n",
    "- **optimize** the loss function with **SGD**\n",
    "- **visualize** the final learned weights\n",
    "    \n",
    "-为SVM实现完全矢量化的**损失函数**\n",
    "\n",
    "-实现其**解析梯度**的完全矢量化表达式\n",
    "\n",
    "-使用数字梯度**检查您的实现**\n",
    "\n",
    "-使用验证集来**调整学习率和正则化强度**\n",
    "\n",
    "-通过**SGD** **优化**损失函数\n",
    "\n",
    "-**可视化**最终学习的权重\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the\n",
    "# notebook rather than in a new window.\n",
    "# 使matplotlib图形在笔记本中inline显示，而不是在新窗口中，是一种魔术。\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0)  # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# 更多魔术，以便笔记本将重新加载外部python模块；\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "## CIFAR-10 Data Loading and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "# cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "cifar10_dir = 'E:\\cifar-10-batches-py'\n",
    "\n",
    "# 清理变量以防止多次加载数据（这可能会导致内存问题）\n",
    "# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "try:\n",
    "   del X_train, y_train\n",
    "   del X_test, y_test\n",
    "   print('Clear previously loaded data.')\n",
    "except:\n",
    "   pass\n",
    "\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# 作为健全性检查，我们打印出培训和测试数据的大小。\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 70 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "# 可视化数据集中的一些示例。\n",
    "# 我们展示的每一类训练图像的几个例子。\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 32, 32, 3)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 32, 32, 3)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 32, 32, 3)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train, val, and test sets. In addition we will\n",
    "# create a small development set as a subset of the training data;\n",
    "# we can use this for development so our code runs faster.\n",
    "# 将数据拆分为训练，验证和测试集。\n",
    "# 另外，我们将创建一个小的开发集作为培训数据的子集；\n",
    "# 我们可以将其用于开发，以便我们的代码运行更快。\n",
    "num_training = 49000\n",
    "num_validation = 1000\n",
    "num_test = 1000\n",
    "num_dev = 500\n",
    "\n",
    "# Our validation set will be num_validation points from the original\n",
    "# training set.\n",
    "# 我们的验证集将是原始训练集中的 num_validation 个点。\n",
    "mask = range(num_training, num_training + num_validation)\n",
    "X_val = X_train[mask]\n",
    "y_val = y_train[mask]\n",
    "\n",
    "# Our training set will be the first num_train points from the original\n",
    "# training set.\n",
    "# 我们的训练集将是原始训练集的 前num_train 个点。\n",
    "mask = range(num_training)\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "# We will also make a development set, which is a small subset of\n",
    "# the training set.\n",
    "# 我们还将制作一个开发集，它是训练集的一小部分。\n",
    "mask = np.random.choice(num_training, num_dev, replace=False)\n",
    "X_dev = X_train[mask]\n",
    "y_dev = y_train[mask]\n",
    "\n",
    "# We use the first num_test points of the original test set as our\n",
    "# test set.\n",
    "# 我们使用原始测试集的 前num_test 个点作为测试集。\n",
    "mask = range(num_test)\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (49000, 3072)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Test data shape:  (1000, 3072)\n",
      "dev data shape:  (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
    "\n",
    "# As a sanity check, print out the shapes of the data\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('dev data shape: ', X_dev.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[130.64189796 135.98173469 132.47391837 130.05569388 135.34804082\n",
      " 131.75402041 130.96055102 136.14328571 132.47636735 131.48467347]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 3073) (1000, 3073) (1000, 3073) (500, 3073)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: subtract the mean image\n",
    "# first: compute the image mean based on the training data\n",
    "# 预处理：减去平均图像\n",
    "# 第一：根据训练数据计算图像均值\n",
    "mean_image = np.mean(X_train, axis=0)\n",
    "print(mean_image[:10]) # print a few of the elements\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.imshow(mean_image.reshape((32,32,3)).astype('uint8')) # visualize the mean image\n",
    "plt.show()\n",
    "\n",
    "# 第二：从训练和测试数据中减去平均图像\n",
    "# second: subtract the mean image from train and test data\n",
    "X_train -= mean_image\n",
    "X_val -= mean_image\n",
    "X_test -= mean_image\n",
    "X_dev -= mean_image\n",
    "\n",
    "# 第三：附加一个bias维度（即bias技巧），以便我们的SVM仅需担心优化单个权重矩阵W。\n",
    "# third: append the bias dimension of ones (i.e. bias trick) so that our SVM\n",
    "# only has to worry about optimizing a single weight matrix W.\n",
    "X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
    "X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
    "X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
    "X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
    "\n",
    "print(X_train.shape, X_val.shape, X_test.shape, X_dev.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Classifier\n",
    "\n",
    "Your code for this section will all be written inside **cs231n/classifiers/linear_svm.py**. \n",
    "\n",
    "As you can see, we have prefilled the function `compute_loss_naive` which uses for loops to evaluate the multiclass SVM loss function. \n",
    "\n",
    "您本节的代码将全部写在 **cs231n/classifiers/linear_svm.py**中。\n",
    "\n",
    "如您所见，我们已经预填充了函数`compute_loss_naive`，该函数使用for循环来评估多类SVM损失函数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 8.494962\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the naive implementation of the loss we provided for you:\n",
    "# 评估我们为您提供的损失的naive实现：\n",
    "from cs231n.classifiers.linear_svm import svm_loss_naive\n",
    "import time\n",
    "\n",
    "# 生成随机SVM权重矩阵of small numbers\n",
    "# generate a random SVM weight matrix of small numbers\n",
    "W = np.random.randn(3073, 10) * 0.0001 \n",
    "\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "print('loss: %f' % (loss, ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `grad` returned from the function above is right now all zero. \n",
    "\n",
    "Derive and implement the gradient for the SVM cost function and implement it inline inside the function `svm_loss_naive`. \n",
    "\n",
    "You will find it helpful to interleave your new code inside the existing function.\n",
    "\n",
    "从上面的函数返回的grad现在都为零。 \n",
    "\n",
    "推导并实现 SVM cost 函数的梯度，并在函数`svm_loss_naive`中inline实现。 \n",
    "\n",
    "您会发现将新代码插入现有函数中会很有帮助。\n",
    "\n",
    "To check that you have correctly implemented the gradient correctly, you can numerically estimate the gradient of the loss function and compare the numeric estimate to the gradient that you computed. \n",
    "\n",
    "We have provided code that does this for you:\n",
    "\n",
    "为了检查您是否正确正确地实现了梯度，可以对损失函数的梯度进行数字估计，并将数字估计与您计算出的梯度进行比较。\n",
    "\n",
    "我们提供了为您执行此操作的代码："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numerical: -21.301222 analytic: -21.301222, relative error: 1.086159e-11\n",
      "numerical: -1.602184 analytic: -1.602184, relative error: 5.151163e-11\n",
      "numerical: -2.230731 analytic: -2.230731, relative error: 9.565790e-11\n",
      "numerical: -27.920813 analytic: -27.920813, relative error: 3.782028e-12\n",
      "numerical: 11.604119 analytic: 11.604119, relative error: 1.104693e-11\n",
      "numerical: -10.439158 analytic: -10.439158, relative error: 1.956881e-11\n",
      "numerical: 13.778446 analytic: 13.796678, relative error: 6.611642e-04\n",
      "numerical: -4.677813 analytic: -4.677813, relative error: 3.811459e-12\n",
      "numerical: -9.546139 analytic: -9.448746, relative error: 5.127311e-03\n",
      "numerical: 33.901458 analytic: 33.901458, relative error: 2.317758e-12\n",
      "numerical: 19.448020 analytic: 19.454249, relative error: 1.601115e-04\n",
      "numerical: 18.933607 analytic: 18.925860, relative error: 2.046266e-04\n",
      "numerical: 1.246002 analytic: 1.241020, relative error: 2.003260e-03\n",
      "numerical: -7.075070 analytic: -7.077927, relative error: 2.018515e-04\n",
      "numerical: 27.861009 analytic: 27.862477, relative error: 2.634831e-05\n",
      "numerical: 13.567947 analytic: 13.481631, relative error: 3.191031e-03\n",
      "numerical: 0.653354 analytic: 0.652454, relative error: 6.889119e-04\n",
      "numerical: -25.688721 analytic: -25.780191, relative error: 1.777186e-03\n",
      "numerical: -23.775867 analytic: -23.845877, relative error: 1.470137e-03\n",
      "numerical: 14.699213 analytic: 14.754556, relative error: 1.878966e-03\n"
     ]
    }
   ],
   "source": [
    "# Once you've implemented the gradient, recompute it with the code below\n",
    "# and gradient check it with the function we provided for you\n",
    "# 一旦你已经实现了梯度，用下面的代码重新计算它和用我们为您提供的函数检查梯度\n",
    "# Compute the loss and its gradient at W.\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# 沿几个随机选择的维度对梯度进行数值计算，然后将其与解析计算出的梯度进行比较。 \n",
    "# 这些数字应在所有维度上几乎完全匹配。\n",
    "# Numerically compute the gradient along several randomly chosen dimensions, and\n",
    "# compare them with your analytically computed gradient. The numbers should match\n",
    "# almost exactly along all dimensions.\n",
    "from cs231n.gradient_check import grad_check_sparse\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)\n",
    "\n",
    "# 在启用正则化功能的情况下再次进行梯度检查，您是否忘记了正则化梯度？\n",
    "# do the gradient check once again with regularization turned on\n",
    "# you didn't forget the regularization gradient did you?\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 5e1)\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 5e1)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 1**\n",
    "\n",
    "It is possible that once in a while a dimension in the gradcheck will not match exactly. \n",
    "\n",
    "What could such a discrepancy be caused by? \n",
    "\n",
    "Is it a reason for concern? \n",
    "\n",
    "What is a simple example in one dimension where a gradient check could fail? \n",
    "\n",
    "How would change the margin affect of the frequency of this happening? \n",
    "\n",
    "*Hint: the SVM loss function is not strictly speaking differentiable*\n",
    "\n",
    "有时gradcheck中的尺寸可能会不完全匹配。 \n",
    "\n",
    "这种差异是由什么引起的？ \n",
    "\n",
    "这是引起关注的原因吗？ \n",
    "\n",
    "一个简单的示例在一维中梯度检查可能失败？ \n",
    "\n",
    "如何改变这种情况发生频率的边际效应?\n",
    "\n",
    "*提示：SVM丢失功能严格来说是不可区分的*\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ *fill this in.*  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss: 8.494962e+00 computed in 0.147247s\n",
      "Vectorized loss: 8.494962e+00 computed in 0.009999s\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Next implement the function svm_loss_vectorized; \n",
    "# for now only compute the loss;\n",
    "# we will implement the gradient in a moment.\n",
    "# 接下来实现svm_loss_vectorized函数;现在只计算损失;\n",
    "# 我们马上就会实现gradient\n",
    "tic = time.time()\n",
    "loss_naive, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss: %e computed in %fs' % (loss_naive, toc - tic))\n",
    "\n",
    "from cs231n.classifiers.linear_svm import svm_loss_vectorized\n",
    "tic = time.time()\n",
    "loss_vectorized, _ = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))\n",
    "\n",
    "# 损失应该匹配，但是向量化实现应该快得多。\n",
    "# The losses should match but your vectorized implementation should be much faster.\n",
    "print('difference: %f' % (loss_naive - loss_vectorized))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss and gradient: computed in 0.148904s\n",
      "Vectorized loss and gradient: computed in 0.003999s\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Complete the implementation of svm_loss_vectorized, and compute the gradient\n",
    "# of the loss function in a vectorized way.\n",
    "# 完成svm_loss_vectorized的实现，并以矢量化的方式计算损失函数的梯度。\n",
    "# The naive implementation and the vectorized implementation should match, but\n",
    "# the vectorized version should still be much faster.\n",
    "# 简单的实现和向量化的实现应该匹配，但是向量化的版本仍然要快得多。\n",
    "tic = time.time()\n",
    "_, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "tic = time.time()\n",
    "_, grad_vectorized = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "# The loss is a single number, so it is easy to compare the values computed\n",
    "# by the two implementations. The gradient on the other hand is a matrix, so\n",
    "# we use the Frobenius norm to compare them.\n",
    "# 损失是单个数字，因此很容易比较两个实现计算的值。\n",
    "# 另一方面，梯度是一个矩阵，所以我们用 Frobenius范数 来比较它们。\n",
    "difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
    "print('difference: %f' % difference)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stochastic Gradient Descent\n",
    "\n",
    "We now have vectorized and efficient expressions for the loss, the gradient and our gradient matches the numerical gradient. \n",
    "\n",
    "We are therefore ready to do SGD to minimize the loss.\n",
    "\n",
    "我们现在有了矢量化的有效的损失表达式，梯度和我们的梯度匹配的数值梯度。\n",
    "\n",
    "因此，我们准备做SGD以减少损失。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1500: loss 408.682255\n",
      "iteration 100 / 1500: loss 241.768560\n",
      "iteration 200 / 1500: loss 147.290024\n",
      "iteration 300 / 1500: loss 91.026876\n",
      "iteration 400 / 1500: loss 57.461883\n",
      "iteration 500 / 1500: loss 36.704233\n",
      "iteration 600 / 1500: loss 23.297398\n",
      "iteration 700 / 1500: loss 16.303271\n",
      "iteration 800 / 1500: loss 11.627644\n",
      "iteration 900 / 1500: loss 9.048540\n",
      "iteration 1000 / 1500: loss 7.291767\n",
      "iteration 1100 / 1500: loss 6.757191\n",
      "iteration 1200 / 1500: loss 6.336034\n",
      "iteration 1300 / 1500: loss 5.733105\n",
      "iteration 1400 / 1500: loss 5.444726\n",
      "That took 9.455645s\n"
     ]
    }
   ],
   "source": [
    "# In the file linear_classifier.py, implement SGD in the function\n",
    "# LinearClassifier.train() and then run it with the code below.\n",
    "from cs231n.classifiers import LinearSVM\n",
    "svm = LinearSVM()\n",
    "tic = time.time()\n",
    "loss_hist = svm.train(X_train, y_train, learning_rate=1e-7, reg=2.5e4,\n",
    "                      num_iters=1500, verbose=True)\n",
    "toc = time.time()\n",
    "print('That took %fs' % (toc - tic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A useful debugging strategy is to plot the loss as a function of\n",
    "# iteration number:\n",
    "# 一个有用的调试策略是把损失描绘成迭代次数的函数:\n",
    "plt.plot(loss_hist)\n",
    "plt.xlabel('Iteration number')\n",
    "plt.ylabel('Loss value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy: 0.378673\n",
      "validation accuracy: 0.385000\n"
     ]
    }
   ],
   "source": [
    "# Write the LinearSVM.predict function and evaluate the performance on both the\n",
    "# training and validation set\n",
    "# 编写 LinearSVM.predict函数并评估训练集和验证集的性能\n",
    "y_train_pred = svm.predict(X_train)\n",
    "print('training accuracy: %f' % (np.mean(y_train == y_train_pred), ))\n",
    "y_val_pred = svm.predict(X_val)\n",
    "print('validation accuracy: %f' % (np.mean(y_val == y_val_pred), ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zzz\\Desktop\\spring1819_assignment123\\assignment1\\cs231n\\classifiers\\linear_svm.py:114: RuntimeWarning: overflow encountered in double_scalars\n",
      "  loss += 0.5 * reg * np.sum(W * W) # 正则化损失（regularization loss）\n",
      "D:\\anacondathree\\lib\\site-packages\\numpy\\core\\fromnumeric.py:86: RuntimeWarning: overflow encountered in reduce\n",
      "  return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n",
      "C:\\Users\\zzz\\Desktop\\spring1819_assignment123\\assignment1\\cs231n\\classifiers\\linear_svm.py:114: RuntimeWarning: overflow encountered in multiply\n",
      "  loss += 0.5 * reg * np.sum(W * W) # 正则化损失（regularization loss）\n",
      "C:\\Users\\zzz\\Desktop\\spring1819_assignment123\\assignment1\\cs231n\\classifiers\\linear_svm.py:109: RuntimeWarning: overflow encountered in subtract\n",
      "  margin = np.maximum(0, scores - correct_class_scores + 1) # (N, C)\n",
      "C:\\Users\\zzz\\Desktop\\spring1819_assignment123\\assignment1\\cs231n\\classifiers\\linear_svm.py:109: RuntimeWarning: invalid value encountered in subtract\n",
      "  margin = np.maximum(0, scores - correct_class_scores + 1) # (N, C)\n",
      "C:\\Users\\zzz\\Desktop\\spring1819_assignment123\\assignment1\\cs231n\\classifiers\\linear_svm.py:166: RuntimeWarning: invalid value encountered in greater\n",
      "  margin[margin > 0] = 1  # (N, C)\n",
      "C:\\Users\\zzz\\Desktop\\spring1819_assignment123\\assignment1\\cs231n\\classifiers\\linear_svm.py:167: RuntimeWarning: invalid value encountered in less_equal\n",
      "  margin[margin <= 0] = 0\n",
      "C:\\Users\\zzz\\Desktop\\spring1819_assignment123\\assignment1\\cs231n\\classifiers\\linear_svm.py:172: RuntimeWarning: overflow encountered in multiply\n",
      "  dW += np.dot(X.T, margin) / num_train + reg * W  # D by C (N, D)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr 1.000000e-07 reg 2.500000e+04 train accuracy: 0.375918 val accuracy: 0.393000\n",
      "lr 1.000000e-07 reg 5.000000e+04 train accuracy: 0.364571 val accuracy: 0.370000\n",
      "lr 5.000000e-05 reg 2.500000e+04 train accuracy: 0.151959 val accuracy: 0.149000\n",
      "lr 5.000000e-05 reg 5.000000e+04 train accuracy: 0.100265 val accuracy: 0.087000\n",
      "best validation accuracy achieved during cross-validation: 0.393000\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune hyperparameters (regularization strength and\n",
    "# learning rate). You should experiment with different ranges for the learning\n",
    "# rates and regularization strengths; if you are careful you should be able to\n",
    "# get a classification accuracy of about 0.39 on the validation set.\n",
    "# 使用验证集来调整超参数(正则化强度和学习率)。\n",
    "# 你应该尝试不同的学习速率和正则化强度范围;\n",
    "# 如果您小心的话，您应该能够在验证集上获得大约0.39的分类精度。\n",
    "# Note: you may see runtime/overflow warnings during hyper-parameter search.\n",
    "# This may be caused by extreme values, and is not a bug.\n",
    "# 在超参数搜索期间，您可能会看到runtime/overflow警告。\n",
    "# 这可能是由极端值引起的，而不是一个bug。\n",
    "\n",
    "learning_rates = [1e-7, 5e-5]\n",
    "regularization_strengths = [2.5e4, 5e4]\n",
    "\n",
    "# results is dictionary mapping tuples of the form\n",
    "# (learning_rate, regularization_strength) to tuples of the form\n",
    "# (training_accuracy, validation_accuracy). The accuracy is simply the fraction\n",
    "# of data points that are correctly classified.\n",
    "# 结果是字典将表单的元组(learning_rate、regularization_strength)映射为\n",
    "# 表单的元组(training_accuracy、validation_accuracy)。\n",
    "# 精度就是被正确分类的数据点的分数。\n",
    "results = {}\n",
    "# The highest validation accuracy that we have seen so far.\n",
    "# 迄今为止我们所看到的最高验证精度。\n",
    "best_val = -1\n",
    "# The LinearSVM object that achieved the highest validation rate.\n",
    "# 获得了最高的验证率的线性支持向量机对象\n",
    "best_svm = None\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Write code that chooses the best hyperparameters by tuning on the validation #\n",
    "# set. For each combination of hyperparameters, train a linear SVM on the      #\n",
    "# training set, compute its accuracy on the training and validation sets, and  #\n",
    "# store these numbers in the results dictionary. In addition, store the best   #\n",
    "# validation accuracy in best_val and the LinearSVM object that achieves this  #\n",
    "# accuracy in best_svm.                                                        #\n",
    "#                                                                              #\n",
    "# Hint: You should use a small value for num_iters as you develop your         #\n",
    "# validation code so that the SVMs don't take much time to train; once you are #\n",
    "# confident that your validation code works, you should rerun the validation   #\n",
    "# code with a larger value for num_iters.                                      #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "# 编写代码，通过调整验证集来选择最佳超参数。\n",
    "# 对于每个超参数组合，在训练集上训练一个线性SVM，\n",
    "# 在训练集和验证集上计算它的精度，\n",
    "# 并将这些数字存储在结果字典中。\n",
    "# 此外，在best_val中存储最佳验证精度，\n",
    "# 而在best_svm中存储达到此精度的线性svm对象。\n",
    "# 在开发验证代码时，应该为num_iter使用一个小值，这样svm就不会花费太多时间进行培训;\n",
    "# 一旦您确信您的验证代码可以工作，您就应该为num_iter重新运行带有较大值的验证代码。\n",
    "pass\n",
    "# svm = LinearSVM()\n",
    "# loss_hist = svm.train(X_train, y_train, learning_rate=1e-7, reg=2.5e4,\n",
    "#                       num_iters=1500, verbose=True)\n",
    "for rs in regularization_strengths:\n",
    "    for lr in learning_rates:\n",
    "        svm = LinearSVM()\n",
    "        loss_hist = svm.train(X_train, y_train, lr, rs, num_iters=3000)\n",
    "        y_train_pred = svm.predict(X_train)\n",
    "        train_accuracy = np.mean(y_train == y_train_pred)\n",
    "        y_val_pred = svm.predict(X_val)\n",
    "        val_accuracy = np.mean(y_val == y_val_pred)\n",
    "        if val_accuracy > best_val:\n",
    "            best_val = val_accuracy\n",
    "            best_svm = svm           \n",
    "        results[(lr,rs)] = train_accuracy, val_accuracy\n",
    "        \n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "        lr, reg, train_accuracy, val_accuracy))\n",
    "\n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the cross-validation results\n",
    "import math\n",
    "x_scatter = [math.log10(x[0]) for x in results]\n",
    "y_scatter = [math.log10(x[1]) for x in results]\n",
    "\n",
    "# plot training accuracy\n",
    "marker_size = 100\n",
    "colors = [results[x][0] for x in results]\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors, cmap=plt.cm.coolwarm)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 training accuracy')\n",
    "\n",
    "# plot validation accuracy\n",
    "colors = [results[x][1] for x in results] # default size of markers is 20\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors, cmap=plt.cm.coolwarm)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear SVM on raw pixels final test set accuracy: 0.372000\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the best svm on test set\n",
    "y_test_pred = best_svm.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print('linear SVM on raw pixels final test set accuracy: %f' % test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 10 Axes>"
      ]
     },
     "metadata": {
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    "# Visualize the learned weights for each class.\n",
    "# Depending on your choice of learning rate and regularization strength, these may\n",
    "# or may not be nice to look at.\n",
    "# 为每个类可视化学习权重。\n",
    "# 根据您对学习速率和正则化强度的选择，这些可能看起来很好，也可能不太好。\n",
    "w = best_svm.W[:-1,:] # strip out the bias\n",
    "w = w.reshape(32, 32, 3, 10)\n",
    "w_min, w_max = np.min(w), np.max(w)\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i + 1)\n",
    "      \n",
    "    # Rescale the weights to be between 0 and 255\n",
    "    wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
    "    plt.imshow(wimg.astype('uint8'))\n",
    "    plt.axis('off')\n",
    "    plt.title(classes[i])"
   ]
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    "**Inline question 2**\n",
    "\n",
    "Describe what your visualized SVM weights look like, and offer a brief explanation for why they look they way that they do.\n",
    "\n",
    "描述您的可视化支持向量机权值是什么样子的，并提供一个简短的解释为什么它们看起来是这样的。\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ *fill this in*  \n"
   ]
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      "[[1 2 3]\n",
      " [4 5 6]]\n",
      "[array([1, 2, 3]), array([4, 5, 6])]\n",
      "[ 6 15]\n",
      "2\n",
      "[1 1 1]\n",
      "[2 2]\n"
     ]
    }
   ],
   "source": [
    "# yyyyyyy=np.array([[1,2,3],[4,5,6]])\n",
    "# print(yyyyyyy)\n",
    "# print(list(yyyyyyy))\n",
    "# print(np.sum(yyyyyyy, axis=1))\n",
    "# print(len(yyyyyyy))\n",
    "\n",
    "# print(np.argmax(yyyyyyy, axis = 0))\n",
    "# print(np.argmax(yyyyyyy, axis = 1))"
   ]
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